Full-Time Principal AI Engineer

We're building the infrastructure layer that will power the next generation of AI agents: collective intelligence.

Entourage is a shared memory protocol that lets AI agents learn from each other's discoveries, building collective intelligence that gets smarter with every interaction.

• Lead the end-to-end design and development of core AI systems that enable collective learning and shared memory among autonomous agents.
• Architect and implement scalable distributed infrastructure for capturing, validating, and surfacing agent experiences across complex networks at scale..
• Drive innovation in protocol-level mechanisms for memory curation, knowledge consolidation, and token-incentivized participation across mutually distrusting agents.
• Build frameworks and tooling that allow agents to transform episodic episodic experiences and action trajectories into reusable, network-wide intelligence.
• Collaborate closely with the CTO to operationalize cutting-edge work in reinforcement learning, LLMs, and multi-agent coordination into production-grade systems.
• Define and uphold technical standards for code quality, security, reliability and scalability across the AI and protocol layers.

• Previous experience in AI architectures and infrastructure, with a proven track record of delivering complex software platforms and AI-native products.
• Proven track record of shipping production systems or prototypes at high velocity, ideally in startup or research contexts where speed and adaptability are paramount.
• Deep expertise in Generative AI, multi-agent systems, LLMs, end-to-end MLOps, and AI infrastructure.
• Active interest and awareness of SOTA in multi-agent systems, collective learning, AI safety, secure agent execution, emerging AI agent architectures, and tool integration.
• Familiarity with one or more multi-agent frameworks (such as LangGraph, LangChain, CrewAI, AutoGen, and Pydantic AI), communication standards (such as MCP, A2A, Story’s Agent TCP/IP, Near’s AITP), distributed systems, distributed AI architectures, and consensus mechanisms.
• Ability to lead and supervise other engineers and AI scientists; this role is expected to grow into a leadership position and is not limited to individual contribution.
• Strategic thinking about platform adoption, scalable developer ecosystems, and familiarity with the Blockchain, Web3, and tokenomics (beneficial but not required).
• Postgraduate degree in a STEM field (Master’s required; PhD strongly preferred).

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